In recent years, the world has witnessed explosive growth in the demand for all types of communications and it is predicted that this demand will increase in the future. In order to communicate information accurately and efficiently, communications receivers are designed to extract information from the channel as best they can. Receivers are designed to operate optimally with certain types of channels, e.g., power line, IR, RF, optical, twisted pair, coax, etc. Various types of noise, however, are a characteristic feature of most channels. Well-known types of channel noise include Intersymbol Interference (ISI) caused by pulse spreading in the channel, fading, intermodulation distortion, multipath, etc. Performance is severely affected in receivers not designed to adequately handle such types of channel noise.
Equalization is a well-known signal processing technique used to combat the Intersymbol Interference distortion imparted to the transmitted signal by the channel whereby the receiver attempts to compensate for the effects of the channel on the transmitted symbols. An equalizer attempts to determine the transmitted data from the received distorted symbols using an estimate of the channel that caused the distortions. Examples of commonly used types of equalizers include the maximum likelihood sequence estimation (MLSE) equalizer that utilizes the well known Viterbi Algorithm (VA), linear equalizer and decision feedback equalizer (DEE). In communications systems where ISI arises due to partial response modulation or a frequency selective channel, a maximum likelihood sequence estimation (MLSE) equalizer is optimal.
Many modern equalization techniques, however, utilize very complex signal processing algorithms to achieve acceptable levels of performance. Practically, most of these techniques can only be performed on expensive high powered digital signal processors. With the ever pressing demand to make communications enabled products smaller, cheaper and high performing, it would be desirable to implement the entire communications hardware in silicon, such as in an Application Specific Integrated Circuit (ASIC) or the like. To place the complex, sophisticated equalization techniques of the prior art on an ASIC, however, would be nearly impossible in terms of gate count and cost, given today's available gate densities and chip sizes. Prior art equalization techniques require extremely large processing resources and memory in order to implement them in silicon.
Thus, there is a need for an equalization technique that is capable of achieving adequate levels of performance while lending itself for efficient and inexpensive implementation in silicon, e.g., ASICs, etc.